



'''simple docstring'''

import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
			from sagemaker import Session, TrainingJobAnalytics
			from sagemaker.huggingface import HuggingFace

@pytest.mark.skipif(
    literal_eval(os.getenv('''TEST_SAGEMAKER'''   ,      '''False'''     )     ) is not True   ,      reason='''Skipping test because should only be run when releasing minor transformers version'''   ,      )
@pytest.mark.usefixtures('''sm_env'''     )
@parameterized_class(
    [
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_glue.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.g4dn.xlarge''',
            '''results''': {'''train_runtime''': 6_5_0, '''eval_accuracy''': 0.6, '''eval_loss''': 0.9},
        },
        {
            '''framework''': '''tensorflow''',
            '''script''': '''run_tf.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.g4dn.xlarge''',
            '''results''': {'''train_runtime''': 6_0_0, '''eval_accuracy''': 0.3, '''eval_loss''': 0.9},
        },
    ]     )
class 							a__( unittest.TestCase     ):






			def 						_lowercase  (     self						)							->							int:
					if self.framework == "pytorch":
							subprocess.run(
							    f"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split()					,   encoding='utf-8'					,   check=_UpperCAmelCase					,   )
					assert hasattr(self					,   'env'						)






			def 						_lowercase  (     self					,   _UpperCAmelCase=1						)							->							Any:
					# creates estimator
					return HuggingFace(
					    entry_point=self.script					,   source_dir=self.env.test_path					,   role=self.env.role					,   image_uri=self.env.image_uri					,   base_job_name=f"""{self.env.base_job_name}-single"""					,   instance_count=_UpperCAmelCase					,   instance_type=self.instance_type					,   debugger_hook_config=_UpperCAmelCase					,   hyperparameters={**self.env.hyperparameters, 'model_name_or_path': self.model_name_or_path}					,   metric_definitions=self.env.metric_definitions					,   py_version='py36'					,   )






			def 						_lowercase  (     self					,   _UpperCAmelCase						)							->							Any:
					TrainingJobAnalytics(_UpperCAmelCase						).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""						)






			def 						_lowercase  (     self						)							->							List[str]:
					# create estimator
					snake_case__	     =self.create_estimator()

					# run training
					estimator.fit()

					# result dataframe
					snake_case__	     =TrainingJobAnalytics(estimator.latest_training_job.name						).dataframe()

					# extract kpis
					snake_case__	     =list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']						)
					snake_case__	     =list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']						)
					# get train time from SageMaker job, this includes starting, preprocessing, stopping
					snake_case__	     =(
					    Session().describe_training_job(estimator.latest_training_job.name						).get('TrainingTimeInSeconds'					,   99_9999						)
					)

					# assert kpis
					assert train_runtime <= self.results["train_runtime"]
					assert all(t >= self.results['eval_accuracy'] for t in eval_accuracy						)
					assert all(t <= self.results['eval_loss'] for t in eval_loss						)

					# dump tests result into json file to share in PR
					with open(f"""{estimator.latest_training_job.name}.json"""					,   'w'						) as outfile:
							json.dump({'train_time': train_runtime, 'eval_accuracy': eval_accuracy, 'eval_loss': eval_loss}					,   _UpperCAmelCase						)









'''simple docstring'''

from timeit import timeit






def 				a  (     UpperCamelCase_    :    int			)       ->	int:
		if number < 0:
				raise ValueError('the value of input must not be negative'			)
		snake_case__	     =0
		while number:
				number &= number - 1
				result += 1
		return result






def 				a  (     UpperCamelCase_    :    int			)       ->	int:
		if number < 0:
				raise ValueError('the value of input must not be negative'			)
		snake_case__	     =0
		while number:
				if number % 2 == 1:
						result += 1
				number >>= 1
		return result






def 				a  (     )       ->	None:

		def do_benchmark(UpperCamelCase_    :    int			) -> None:
				snake_case__	     ='import __main__ as z'
				print(f"""Benchmark when {number = }:"""			)
				print(f"""{get_set_bits_count_using_modulo_operator(UpperCamelCase_			) = }"""			)
				snake_case__	     =timeit('z.get_set_bits_count_using_modulo_operator(25)'      ,					setup=UpperCamelCase_			)
				print(f"""timeit() runs in {timing} seconds"""			)
				print(f"""{get_set_bits_count_using_brian_kernighans_algorithm(UpperCamelCase_			) = }"""			)
				snake_case__	     =timeit(
				    'z.get_set_bits_count_using_brian_kernighans_algorithm(25)'      ,					setup=UpperCamelCase_      ,					)
				print(f"""timeit() runs in {timing} seconds"""			)

		for number in (25, 37, 58, 0):
				do_benchmark(UpperCamelCase_			)
				print()


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			benchmark()




